Predictive Sales Analysis in Coffee Shops Using the Random Forest Algorithm
The coffee shop industry has experienced significant growth, evolving into a highly competitive marketplace demanding specialty coffee and personalized experiences. While data-driven strategies are crucial for optimizing operations, many owners still struggle to effectively leverage their sales data...
Uloženo v:
| Vydáno v: | MALCOM: Indonesian Journal of Machine Learning and Computer Science Ročník 5; číslo 3; s. 1000 - 1011 |
|---|---|
| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
31.07.2025
|
| ISSN: | 2797-2313, 2775-8575 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Shrnutí: | The coffee shop industry has experienced significant growth, evolving into a highly competitive marketplace demanding specialty coffee and personalized experiences. While data-driven strategies are crucial for optimizing operations, many owners still struggle to effectively leverage their sales data to understand dynamic customer behavior and enhance decision-making. Addressing this gap, this study explores the application of machine learning (ML) techniques, specifically the Random Forest Regressor model, to predict sales performance within the coffee shop business environment. By analyzing factors such as transaction timing, store location, product type, and day of the week, this research aims to uncover patterns that can enhance inventory management and customer engagement. The Random Forest model was evaluated through cross-validation, yielding a mean Mean Squared Error (MSE) of 80.97, which indicates moderate predictive accuracy and represents an improvement over traditional forecasting methods commonly employed in the industry. Feature importance analysis revealed that Premium Beans is the most influential predictor, followed by seasonal trends (month), time of day, and weekend sales patterns. These findings underscore the importance of incorporating temporal and contextual factors into forecasting models. |
|---|---|
| ISSN: | 2797-2313 2775-8575 |
| DOI: | 10.57152/malcom.v5i3.2023 |